Correlation Between Fecal Microbiota and Corticosteroid Responsiveness in Primary Immune Thrombocytopenia: an Exploratory Study

Corticosteroids (CSs) are the initial therapy for immune thrombocytopenia (ITP); however, their efficacy is not adequately predicted. As a novel biomarker, the composition of the gut microbiota is non-invasively tested and altered in patients with ITP. This study aims to develop a predictive model that leverages gut microbiome data to predict the CS response in patients with ITP within the initial four weeks of treatment. Metagenomic sequencing is performed on fecal samples from 212 patients with ITP, 152 of whom underwent CS treatment and follow-up. Predictive models are trained using six machine-learning algorithms, integrating clinical indices and gut microbiome data. The support vector machine (SVM) algorithm-based model has the highest accuracy (AUC = 0.80). This model utilized a comprehensive feature set that combined clinical data (including sex, age, duration, platelet count, and bleeding scales) with selected microbial species (including Bacteroides ovatus, Bacteroides xylanisolvens, and Parabacteroides gordonii), alpha diversities, KEGG pathways, and microbial modules. This study will provide new ideas for the prediction of clinical CS efficacy, enabling informed decision-making regarding the initiation of CS or personalized treatment in patients with ITP.

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Publication Details

Journal
Advanced Science
Published
2025-03-05
DOI
https://doi.org/10.1002/advs.202410417
Citations
2
Primary Topic
Platelet Disorders and Treatments
Type
article
Field-Weighted Citation Impact
3.54

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article

Correlation Between Fecal Microbiota and Corticosteroid Responsiveness in Primary Immune Thrombocytopenia: an Exploratory Study

Yuxiu Chen, Jia Feng, Jinhai Ren, Zhong‐Xing Jiang et al.
2 citations
Advanced Science
Platelet Disorders and Treatments
3.54
article

Correlation Between Fecal Microbiota and Corticosteroid Responsiveness in Primary Immune Thrombocytopenia: an Exploratory Study

Yuxiu Chen, Jia Feng, Jinhai Ren, Zhong‐Xing Jiang, Bang‐Shuo Zhang, Xiao‐Jun Huang, Haixia Fu, Jianlin Qiao, Xiaoyu Zhu, Jianying Zhou, Hebing Zhou, Zhuo‐Yu An, Qian‐Fei Wang, Mengtong Zang, Li Yu, Yunfeng Cheng, Qingyuan Qu, Hongmei Jing, Da Gao, Zhuo‐Gang Liu, Xin Wang, Qi Chen, Fan Yu, Lulu Wang, Wei Li, Menglin Li, Fengqi Liu, Yueying Li, Rui‐Bin Huang, Yejun Wu, Yi Liu, Lijuan Cui, Yuhua Li, Dong Hu, Xiaohui Zhang, Qiu‐Sha Huang, Ru Feng, M. Xiao
article en
2 citations

Abstract

Corticosteroids (CSs) are the initial therapy for immune thrombocytopenia (ITP); however, their efficacy is not adequately predicted. As a novel biomarker, the composition of the gut microbiota is non-invasively tested and altered in patients with ITP. This study aims to develop a predictive model that leverages gut microbiome data to predict the CS response in patients with ITP within the initial four weeks of treatment. Metagenomic sequencing is performed on fecal samples from 212 patients with ITP, 152 of whom underwent CS treatment and follow-up. Predictive models are trained using six machine-learning algorithms, integrating clinical indices and gut microbiome data. The support vector machine (SVM) algorithm-based model has the highest accuracy (AUC = 0.80). This model utilized a comprehensive feature set that combined clinical data (including sex, age, duration, platelet count, and bleeding scales) with selected microbial species (including Bacteroides ovatus, Bacteroides xylanisolvens, and Parabacteroides gordonii), alpha diversities, KEGG pathways, and microbial modules. This study will provide new ideas for the prediction of clinical CS efficacy, enabling informed decision-making regarding the initiation of CS or personalized treatment in patients with ITP.

Advanced ScienceVol. 12(22)
Hebei Medical University (CN), University of Science and Technology of China (CN), Nanchang University (CN), Sun Yat-sen University (CN), Chongqing University (CN), Xuzhou Medical College (CN), Chinese Academy of Sciences (CN), Peking University (CN), Fudan University (CN), Beijing Institute of Genomics (CN), Chinese PLA General Hospital (CN), Second Hospital of Hebei Medical University (CN), Second Affiliated Hospital of Nanchang University (CN), Beijing Luhe Hospital Affiliated to Capital Medical University (CN), Shandong Provincial Hospital (CN), Beijing Hospital (CN), Zhujiang Hospital (CN), Peking University People's Hospital (CN), Zhongshan Hospital (CN), Ningxia Medical University (CN), Peking University Shenzhen Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Peking University Third Hospital (CN), First Bethune Hospital of Jilin University (CN), Beijing Tsinghua Chang Gung Hospital (CN), First Affiliated Hospital of Zhengzhou University (CN), Center for Life Sciences (CN), Shandong First Medical University (CN), First Affiliated Hospital of Nanchang University (CN), University of Chinese Academy of Sciences (CN), Inner Mongolia Medical University (CN), Southern Medical University (CN), China Medical University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Natural Science Foundation of Beijing Municipality, Jiangsu Provincial Department of Human Resources and Social Security, Capital Health Research and Development of Special Fund, Key Technologies Research and Development Program
Peace, Justice and strong institutions
Openalex Percentile: Top 9%
Platelet Disorders and Treatments
3.54
DOIOpen Access PDFOpenAlex
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